The rescission of behaviors: Inconsistent responses in adolescent sexuality data

In longitudinal survey research, certain questions can be rescinded illogically. For instance, respondents who at Time 1 report having had sexual intercourse may at Time 2 report never having done so. This paper reports measurement techniques and analyses of these types of inconsistencies from an ongoing longitudinal adolescent sexuality project. Inconsistencies in intercourse, masturbation, and other sexual behaviors are reported and compared to rates from other studies and other less sensitive behaviors within the same study. Three conclusions are presented: (1) inconsistencies should be considered a natural part of any longitudinal survey process and should be incorporated into the response model; (2) inconsistency rates in these particular data support the contention that adolescent sexuality data of appropriate quality for analytical purposes can be obtained; and (3) inconsistency rates in fact contain substantive information concerning the processes under consideration.     

Urban versus Rural: Fertility Decline in the Cities and Rural Districts of Prussia, 1875 to 1910

Marital fertility in 54 Prussian cities and 407 Prussian Kreise (administrative areas) is analyzed using unusually rich and detailed socioeconomic and demographic data from eight quinquennial census between 1875 and 1910. Pooled cross-section time series methods are used to examine influences on marital fertility level and on marital fertility decline, focusing particularly on fertility differences according to level of urbanization. Increases in female labour force participation rate and income, the growth of financial services and communications, improvement in education, and reduction in infant mortality account for most of the marital fertility decline in 19th century Prussia. In 1875, rural and urban fertility were similar but by 1910, urban fertility was far lower than rural in part because the values of some of these variables changed more rapidly in the cities, and in part because some of these variables had stronger effects in urban settings.     

A divergence test for autoregressive time series models

In this paper, we study the normality test for the innovations of unstable autoregressive models based on the divergence test. In order to investigate the asymptotic behavior of the tests, we use the link between the divergence test and the residual empirical process. Simulation results are provided for illustration.     

The role of public health in population health

The role of public health is central in population health. What distinguishes public health from clinical medicine is that it's focus is on the entire population--not the individual patient. To achieve the goal of population health, healthy communities, or Healthy People 2000 requires physician leadership in medicine and public health at all levels--local, state, and national. The challenge is formidable, but the goals are attainable through strategies that focus on the goal of the common good and through collaboration of public health, medicine, and the community.     

CONVERGENCE AND PREDICTION OF PRINCIPAL COMPONENT SCORES IN HIGH-DIMENSIONAL SETTINGS

A number of settings arise in which it is of interest to predict Principal Component (PC) scores for new observations using data from an initial sample. In this paper, we demonstrate that naive approaches to PC score prediction can be substantially biased towards 0 in the analysis of large matrices. This phenomenon is largely related to known inconsistency results for sample eigenvalues and eigenvectors as both dimensions of the matrix increase. For the spiked eigenvalue model for random matrices, we expand the generality of these results, and propose bias-adjusted PC score prediction. In addition, we compute the asymptotic correlation coefficient between PC scores from sample and population eigenvectors. Simulation and real data examples from the genetics literature show the improved bias and numerical properties of our estimators.     

Orthodox BLUP versus h-likelihood methods for inferences about random effects in Tweedie mixed models

Recently, the orthodox best linear unbiased predictor (BLUP) method was introduced for inference about random effects in Tweedie mixed models. With the use of h-likelihood, we illustrate that the standard likelihood procedures, developed for inference about fixed unknown parameters, can be used for inference about random effects. We show that the necessary standard error for the prediction interval of the random effect can be computed from the Hessian matrix of the h-likelihood. We also show numerically that the h-likelihood provides a prediction interval that maintains a more precise coverage probability than the BLUP method.     

Joint impact of multiple observations on a subset of variables in multiple linear regression

In multiple linear regression analysis, each observation affects the fitted regression equation differently and has varying influences on the regression coefficients of the different variables. Chatterjee & Hadi (1988) have proposed some measures such as DSSE_ij (Impact on Residual Sum of Squares of simultaneously omitting the ith observation and the jth variable), F_j (Partial F-test for the jth variable) and F_j(i) (Partial F-test for the jth variable omitting the ith observation) to show the joint impact and the interrelationship that exists among a variable and an observation. In this paper we have proposed more extended form of those measures DSSE_IJ, F_J and F_J(I) to deal with the interrelationships that exist among the multiple observations and a subset of variables by monitoring the effects of the simultaneous omission of multiple variables and multiple observations.     

A hierarchical model for extreme wind speeds

Summary.  A typical extreme value analysis is often carried out on the basis of simplistic inferential procedures, though the data being analysed may be structurally complex. Here we develop a hierarchical model for hourly gust maximum wind speed data, which attempts to identify site and seasonal effects for the marginal densities of hourly maxima, as well as for the serial dependence at each location. A Gaussian model for the random effects exploits the meteorological structure in the data, enabling increased precision for inferences at individual sites and in individual seasons. The Bayesian framework that is adopted is also exploited to obtain predictive return level estimates at each site, which incorporate uncertainty due to model estimation, as well as the randomness that is inherent in the processes that are involved.     

Multiracial Identity Integration: Perceptions of Conflict and Distance among Multiracial Individuals

This article examines how multiracial individuals negotiate their different and sometimes conflicting racial identities. Drawing from previous work on bicultural identity integration (see   Benet-Martínez & Haritatos, 2005 ), we proposed a new construct, multiracial identity integration (MII), to measure individual differences in perceptions of compatibility between multiple racial identities. We found that MII is composed of two independent subscales: racial distance that describes whether different racial identities are perceived as disparate, and racial conflict that describes whether different racial identities are perceived as in conflict. We also found that recalling positive multiracial experiences increased MII, while recalling negative multiracial experiences decreased MII. These findings have implications for understanding the psychological well-being of multiracial individuals, and the development of social policy and programs catered to this population.